Why “Where” Matters: Geospatial data in sustainable finance

In a recent webinar, Cédric Merle, Head of the Center of Expertise & Innovation, Gratien Davasse, Sustainable Finance & Nature Expert, Anne-Claire Lejeune, Originator & Strategic Sustainability Advisory Lead, and Amaury de Balincourt, Expert in Climate, Environment, and Sustainable Finance at Natixis CIB’s Green & Sustainable Hub discussed the analyses from their newly released study ‘Connecting the Dots’ on geospatial big data. Their argument is clear: in sustainable finance, understanding where assets are located is becoming just as important as understanding what they are. By combining geospatial data with other datasets and methodologies, the Green & Sustainable Hub looks at enhancing both risk modelling and sustainability frameworks.

In sustainable finance, the “what” of project finance is inseparable from the “where” as renewable energy infrastructure must be matched up to the location of the energy source.

As climate adaptation and physical risks mitigation become central to project finance, the question of “where” becomes increasingly relevant across a much broader set of assets. In turn, answering this question relies on two main data points: asset location and cartographic layers.

Asset location – given as GPS coordinates – can take several forms: a single point for a building, a polygon for an industrial site, or a line spanning hundreds of kilometres in the case of a railway or oil pipeline. Collecting these coordinates increasingly relies on Large Language Models (LLMs), which can extract data on company asset-level data from disclosures and infer supplier-customer relationships to map supply chains.

Cartographic layers provide a second component. These spatial datasets describe environmental and socio-economic contexts, such as flood hazards or access to essential services, using satellite systems and longitudinal modelling to track changes over time.

Combining asset locations with cartographic layers provides a first-order view of physical and nature-related risk. Indicators like asset flood risk, can be refined further by incorporating asset characteristics.

These characteristics describe an asset in greater depth by detailing its form and function, including height, materials, production processes, resource needs, and economic importance. This additional layer enables a more nuanced assessment of risk exposure: for example, two buildings can be located in a flood-prone area, however, one may have valuable equipment on the fifth floor while another is a single story with machinery on the ground level.

Despite its value, this type of data remains a bottleneck. Asset-level characteristics are often incomplete outside regulated sectors, meaning economic assessments still rely heavily on proxies.

By contrast, advances in cartographic layering are enabling more targeted impact investment as the advancement of initiatives like the UN’s Sustainable Development Goals (SDG) relies on identifying localised gaps. Indeed, in Mexico’s SDG bond issuance, geodata underpinned both eligibility criteria and impact reporting by identifying regions with the greatest needs. Similarly, BPCE’s social impact bonds incorporate location-based criteria to direct investments towards areas with the highest economic and employment needs.

Beyond technical progress, regulations and industry uptake have spurred the leap in geodata access. In Europe, financial institutions are increasingly recognising the risk management value of geodata as part of a wider shift towards data-driven supervision. The European Central Bank, for instance, leverages geodata to map regional systemic risks, including real estate concentration and climate vulnerability.

In parallel, the European Banking Authority requires institutions to incorporate location-based dimensions into stress testing. Going a step further, the Network for Greening the Financial System (NGFS) is integrating region and asset-level geographies in its scenarios to reflect spatial heterogeneity in risk.

These scenarios address biodiversity loss, ecosystem service degradation, land use change and natural capital depletion, using spatial datasets to identify where risk is most likely to emerge. The NGFS encourages banks to translate these insights into probabilities of default, loss given default, and exposure metrics at regional or facility level.

As advanced data sets bridge the gap between corporate level synthetic biodiversity footprints and site level assessments, they open the way for nature stress testing of corporate assets through ecosystem degradation and its impact on production processes. Recently, research on sophisticated nature stress testing models has better captured ecosystem complexity by accounting for an expanded set of factors including soil, air, vegetation and species.

Recently, Côte d’Ivoire and the world Bank structured a sustainability-linked loan, where geodata play a central role in monitoring forest-related targets. Satellite imagery tracking biomass and land-use change feeds directly into KPI reporting.

Similarly, the EU Deforestation Regulation (EUDR), is expected to push corporates to integrate geospatial analytics into their due diligence processes, particularly for supply chain risk.

Geodata also provides insight into industrial location decisions. For example in Chile, the concentration of data centres in Santiago is supported by multiple spatial factors: access to low-carbon electricity, proximity to submarine cables and favourable economic policies. However, geospatial analysis highlights emerging risks. Water stress in the region poses a long-term challenge, particularly as data centre demand increases while precipitation declines.

To mitigate these risks, are implementing measures to build the resilience of their own operations. For example, Google has developed new geospatial tools using satellite and weather data to provide farmers with real time irrigation recommendations, helping to stabilise water consumption at a regional level.

Geodata is reshaping how investors approach risks, opportunities identification, strategy setting, compliance, and disclosure. By enabling more granular, localised analysis, geospatial insights reduce information asymmetry.

This significantly amplifies the value of traditional ESG data, whether it's disintermediated sources like sustainability ratings and controversy monitoring, or self-reported information from sustainability reports and CDP disclosures. Crucially, geodata enables investors to construct a detailed, bottom-up view of ESG risk, tracing it from individual assets all the way to diversified portfolios, offering a clearer understanding of how environmental risks might actually materialize.

The proliferation of geodata is transforming sustainable finance. What was once a supplementary dataset is becoming a core component of risk modelling and investment decision-making.

While gaps remain, notably around asset-level characteristics, the direction of travel is clear: as data quality improves and regulation tightens, geospatial intelligence will play an increasingly central role in understanding both risk and opportunity.

Natixis CIB Green Hub is positioned to accompany its clients seizing these new possibilities.